ABSTRACT: There is significant interest in understanding how structural/functional changes in the brain explain symptoms caused by neurodegenerative diseases such as Alzheimer’s disease (AD). Despite clear variations in the brain reported in the literature at the dementia stage of AD, changes in the preclinical stage of AD still remain poorly characterized. Such preclinical datasets are typically small and group differences are subtle, and makes their analyses challenging. This talk will describe some of my recent work to overcome these difficulties in an effort to elucidate how the human brain varies as a function of risk factors, even in asymptomatic individuals. The theory driving these analyses is a multi-resolution scheme for performing statistical analysis of graph structured data in neuroimaging. The framework derives a wavelet representation at each measurement location which captures the graph context at multiple resolutions. Extensive empirical results using statistical group analysis (i.e., diseased vs. controls) show how this framework offers improved statistical power in analyzing graph structured neuroimages such as cortical thickness on brain meshes and tractography derived from Diffusion Tensor Images (DTI) to identify potentially subtle variation in the brain due to AD or AD risk factors